/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */
/*
$begin optimize_reverse_active.cpp$$

$section Example Optimization and Reverse Activity Analysis$$

$code
$srcfile%example/optimize/reverse_active.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++
# include <cppad/cppad.hpp>
namespace {
	struct tape_size { size_t n_var; size_t n_op; };

	template <class Vector> void fun(
		const Vector& x, Vector& y, tape_size& before, tape_size& after
	)
	{	typedef typename Vector::value_type scalar;

		// phantom variable with index 0 and independent variables
		// begin operator, independent variable operators and end operator
		before.n_var = 1 + x.size(); before.n_op  = 2 + x.size();
		after.n_var  = 1 + x.size(); after.n_op   = 2 + x.size();

		// initilized product of even and odd variables
		scalar prod_even = x[0];
		scalar prod_odd  = x[1];
		before.n_var += 0; before.n_op  += 0;
		after.n_var  += 0; after.n_op   += 0;
		//
		// compute product of even and odd variables
		for(size_t i = 2; i < size_t( x.size() ); i++)
		{	if( i % 2 == 0 )
			{	// prod_even will affect dependent variable
				prod_even = prod_even * x[i];
				before.n_var += 1; before.n_op += 1;
				after.n_var  += 1; after.n_op  += 1;
			}
			else
			{	// prod_odd will not affect dependent variable
				prod_odd  = prod_odd * x[i];
				before.n_var += 1; before.n_op += 1;
				after.n_var  += 0; after.n_op  += 0;
			}
		}

		// dependent variable for this operation sequence
		y[0] = prod_even;
		before.n_var += 0; before.n_op  += 0;
		after.n_var  += 0; after.n_op   += 0;
	}
}

bool reverse_active(void)
{	bool ok = true;
	using CppAD::AD;
	using CppAD::NearEqual;
	double eps10 = 10.0 * std::numeric_limits<double>::epsilon();

	// domain space vector
	size_t n  = 6;
	CPPAD_TESTVECTOR(AD<double>) ax(n);
	for(size_t i = 0; i < n; i++)
		ax[i] = AD<double>(i + 1);

	// declare independent variables and start tape recording
	CppAD::Independent(ax);

	// range space vector
	size_t m = 1;
	CPPAD_TESTVECTOR(AD<double>) ay(m);
	tape_size before, after;
	fun(ax, ay, before, after);

	// create f: x -> y and stop tape recording
	CppAD::ADFun<double> f(ax, ay);
	ok &= f.size_var() == before.n_var;
	ok &= f.size_op()  == before.n_op;

	// Optimize the operation sequence
	f.optimize();
	ok &= f.size_var() == after.n_var;
	ok &= f.size_op()  == after.n_op;

	// check zero order forward with different argument value
	CPPAD_TESTVECTOR(double) x(n), y(m), check(m);
	for(size_t i = 0; i < n; i++)
		x[i] = double(i + 2);
	y    = f.Forward(0, x);
	fun(x, check, before, after);
	ok &= NearEqual(y[0], check[0], eps10, eps10);

	return ok;
}

// END C++
